• 정보처리학회논문지B
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• 제11B권2호
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• pp.213-220
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• 2004

진화 알고리즘은 여러 개의 상충하는 목적을 갖는 다목적 최적화 문제를 해결하기에 적합한 방법이다. 특히, 파레토 지배관계에 기초하여 개체의 적합도를 평가하는 파레토 기반 진화알고리즘들은 그 성능에 있어서 우수한 평가를 받고 있다. 최근의 파레토 기반 진화알고리즘들은 전체 파레토 프론트에 균일하게 분포하는 해집합의 생성을 위해 개체들의 밀도를 개체의 적합도를 평가하기 위한 하나의 요소로 사용하고 있다. 그러나 밀도의 역할은 전체 진화과정에서 중요한 요소가 되기보다는 파레토 프론트에 어느 정도 수렴된 후, 개체의 균일 분포를 만들기 위해 사용된다. 본 논문에서 우리는 파레토 지배 순위와 밀도에 대한 임의가중치를 적용한 다목적 최적화 진화알고리즘을 제안한다. 제안한 알고리즘은 진화 개체의 적합도를 평가하기 위해 파레토 순위와 밀도에 대한 임의의 가중치를 적용하므로 전체 진화과정에서 파레토 순위와 밀도가 비슷한 영향을 미치도록 하였다. 또한, 제안한 방법을 6개의 다목적 최적화 문제에 적용한 결과 비교적 우수한 결과를 보였다.

• 정보처리학회논문지B
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• 제11B권7호
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• pp.841-848
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• 2004

진화 알고리즘은 여러 개의 상충하는 목적을 갖는 다목적 최적화 문제를 해결하기에 적합한 방법이다. 특히, 파레토 지배관계에 기초하여 개체의 적합도를 평가하는 파레토 기반 진화알고리즘들은 그 성능에 있어서 비교적 우수한 평가를 받고 있다. 그러나 일반화된 다목적 최적화 진화알고리즘은 복잡한 문제들에서 찾아진 해들의 분포가 전체 파레토 경계면에 대하여 균일하지 못하고 특정 지역에서 집중적으로 해를 생성하는 문제점을 가지고 있다. 본 논문에서 우리는 이러한 문제점을 보완하기 위한 다목적 최적화 진화알고리즘을 제안한다. 제안한 알고리즘은 현재까지 찾아진 최적해들 중 특정 지역에 관중되지 않은 해를 우수 종자로 복제 연산에 참여시킨다. 따라서 특별한 지역탐색 기법을 사용하지 않아도 종자가 되는 개체 주위에 새로운 개체를 생성할 확률이 높기 때문에 지역탐색의 효과를 가질 수 있고, 비교적 고른 분포의 파레토 최적 해를 생성한 수 있다. 5개의 테스트 함수에 대한 실험 결과, 제안한 알고리즘은 모든 문제에서 전체 파레토 경계면에 균일한 분포의 해들을 생성할 수 있었으며, 많은 지역해를 가지는 문제를 제외한 모든 문제에서 NSGA-II보다 우수한 수렴 결과를 보였다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 봄 학술발표회논문집
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• pp.177-186
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• 2000

An improved multi-level（IML) optimization algorithm using automatic differentiation (AD) for multi-objective optimum design of framed structures is proposed in this paper. In order to optimize the steel frames under seismic load, two main objective functions need to be considered for minimizing the structural weight and maximizing the strain energy. For the efficiency of the proposed algorithm, multi-level optimization techniques using decomposition method that separately utilizes both system-level and element-level optimizations and an artificial constraint deletion technique are incorporated in the algorithm. And also to save the numerical efforts, an efficient reanalysis technique through approximated structural responses such as moments, frequencies, and strain energy with respect to intermediate variables is proposed in the paper. Sensitivity analysis of dynamic structural response is executed by AD that is a powerful technique for computing complex or implicit derivatives accurately and efficiently with minimal human effort. The efficiency and robustness of the IML algorithm, compared with a plain multi-level (PML) algorithm, is successfully demonstrated in the numerical examples.

• 한국경영과학회지
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• 제32권1호
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• pp.77-91
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• 2007

In this paper, we present a symbiotic evolutionary algorithm for multi-objective optimization. The goal in multi-objective evolutionary algorithms (MOEAs) is to find a set of well-distributed solutions close to the true Pareto optimal solutions. Most of the existing MOEAs operate one population that consists of individuals representing the entire solution to the problem. The proposed algorithm has a two-leveled structure. The structure is intended to improve the capability of searching diverse and food solutions. At the lower level there exist several populations, each of which represents a partial solution to the entire problem, and at the upper level there is one population whose individuals represent the entire solutions to the problem. The parallel search with partial solutions at the lower level and the Integrated search with entire solutions at the upper level are carried out simultaneously. The performance of the proposed algorithm is compared with those of the existing algorithms in terms of convergence and diversity. The optimization problems with continuous variables and discrete variables are used as test-bed problems. The experimental results confirm the effectiveness of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3293-3311
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• 2015

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5013-5034
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• 2019

In the Peer-to-Peer (P2P) streaming systems, peers randomly form a network overlay to share video resources with a data scheduling scheme. A data scheduling scheme can have a great impact on system performance, which should achieve two optimal objectives at the same time ideally. The two optimization objectives are to improve the perceived video quality and maximize the network throughput, respectively. Maximizing network throughput means improving the utilization of peer's upload bandwidth. However, maximizing network throughput will result in a reduction in the perceived video quality, and vice versa. Therefore, to achieve the above two objects simultaneously, we proposed a new data scheduling scheme based on multi-objective particle swarm optimization data scheduling scheme, called MOPSO-DS scheme. To design the MOPSO-DS scheme, we first formulated the data scheduling optimization problem as a multi-objective optimization problem. Then, a multi-objective particle swarm optimization algorithm is proposed by encoding the neighbors of peers as the position vector of the particles. Through extensive simulations, we demonstrated the MOPSO-DS scheme could improve the system performance effectively.

• 산업경영시스템학회지
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• 제18권33호
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• pp.79-86
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• 1995

This paper intends to develope the multi-objective loading model in a flexible manufacturing system (FMS) to support decision maker under fuzzy environment. To obtain the optimal solution, this paper uses interactive fuzzy multi-objective linear programing(IFMOLP) and describes the process of optimal solution. As a case study, numerical examples are demonstrated to show the effectiveness of the proposed model.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2001년도 추계학술대회 논문집
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• pp.269-272
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• 2001

In this paper, we presents a systematic methodology for minimizing tardiness and maximizing resource utilization in a multi-plant supply chain. A methodology is represented to a multi-objective mathematical program model. The model offers flexible and efficient multi-plant planning and scheduling. Also, We develope a realistic and flexible planning model using the genetic algorithm to solve the model.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2005년도 추계 학술대회논문집
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• pp.1-10
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• 2005

A new approach, Multi-Objective Design Exploration (MODE), is presented to address Multidisciplinary Design Optimization (MDO) problems by CFD-CSD coupling. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists of Kriging Model, Adaptive Range Multi Objective Genetic Algorithms, Analysis of Variance and Self-Organizing Map. The main emphasis of this approach is visual data mining. An MDO system using high fidelity simulation codes, Navier-Stokes solver and NASTRAN, has been developed and applied to a regional-jet wing design. Because the optimization system becomes very computationally expensive, only brief exploration of the design space has been performed. However, data mining result demonstrates that design knowledge can produce a good design even from the brief design exploration.

• 한국유기농업학회지
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• 제26권2호
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• pp.181-202
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• 2018

Most of eco-friendly agrifood processing enterprises in Korean rural area are small and medium-sized business. For this reason, it's hard for eco-friendly agrifood processing enterprises to neither analyze business performance for efficient business management nor establish their own business plan for rational decision-making. Therefore it's necessary to design effective mathematical programming model and to make practical application which can support rational management decision-making ensuring the stable business activity of eco-friendly agrifood processing enterprises. Accordingly this paper focuses on the designing and its application of multi-objective mathematical programming model using goal programming to support rational decision-making of eco-friendly agrifood processing enterprise. Hansalimanseongmachum Food Inc. which runs soy bean processing business making tofu based on regional-based soybean farms around Anseong City will be the specific case to apply multi-objective mathematical programming model in practice. And it will suggest measures to support rational management decision-making of other eco-friendly agrifood processing enterprises.